class_docstr: Targeted maximum likelihood estimation.
  
  Ref: Gruber, S., & Van Der Laan, M. J. (2009). Targeted maximum likelihood estimation: A gentle introduction.
class_docstr_tok: ["Targeted","maximum","likelihood","estimation",".","Ref",":","Gruber",",","S.",",","&","Van","Der","Laan",",","M.","J",".","(","2009",")",".","Targeted","maximum","likelihood","estimation",":","A","gentle","introduction","."]
docstr: Initialize a TMLE learner.
  
  Args:
      learner: a model to estimate the outcome
      ate_alpha (float, optional): the confidence level alpha of the ATE estimate
      control_name (str or int, optional): the name of the control group
      cv (sklearn.model_selection._BaseKFold, optional): sklearn CV object
docstr_tok: ["Initialize","a","TMLE","learner",".","Args",":","learner",":","a","model","to","estimate","the","outcome","ate_alpha","(","float",",","optional",")",":","the","confidence","level","alpha","of","the","ATE","estimate","control_name","(","str","or","int",",","optional",")",":","the","name","of","the","control","group","cv","(","sklearn.model_selection._BaseKFold",",","optional",")",":","sklearn","CV","object"]
code: def __init__(
      self,
      learner,
      ate_alpha=0.05,
      control_name=0,
      cv=None,
      calibrate_propensity=True,
  ):
      """Initialize a TMLE learner.
  
      Args:
          learner: a model to estimate the outcome
          ate_alpha (float, optional): the confidence level alpha of the ATE estimate
          control_name (str or int, optional): the name of the control group
          cv (sklearn.model_selection._BaseKFold, optional): sklearn CV object
      """
      self.model_tau = learner
      self.ate_alpha = ate_alpha
      self.control_name = control_name
      self.cv = cv
      self.calibrate_propensity = calibrate_propensity
  
file_path: causalml/causalml/inference/meta/tmle.py
imports: import logging
  import numpy
  import pandas
  from scipy.optimize import minimize
  from scipy.special import expit, logit
  from scipy.stats import norm
  from sklearn.preprocessing import MinMaxScaler
  from causalml.inference.meta.utils import check_treatment_vector, check_p_conditions, convert_pd_to_np
  from causalml.propensity import calibrate
label_desc: Use image node_id 1 to create a new TMLELearner object from inherited base classes: object with example: obj = TMLELearner(learner, ate_alpha, control_name, cv, calibrate_propensity)